Dependency Parsing with Dilated Iterated Graph CNNs
Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency ParsingGPUSentenceSimilar Papers 제목 키워드 기반
Turning the pipeline into a loop: Iterated unsupervised dependency parsing and PoS induction
Unsupervised Dependency Parsing: Let's Use Supervised Parsers
We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms. Our approach, called `iterated reranking' (IR), starts with dependency trees gen…
Dependency ParsingRerankingUnsupervised Dependency ParsingFast and Accurate Entity Recognition with Iterated Dilated Convolutions
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-direct…
Computational EfficiencyGPUNamed Entity Recognition (NER)NER+1Semi-Supervised Neural System for Tagging, Parsing and Lematization
This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The system consists of jointly trained tagger, lemmatizer, and dependency…
End-to-end Graph-based TAG Parsing with Neural Networks
We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, ou…
POSPOS TaggingTAG